arXiv Machine Learning June 24, 2026 By Chi-Sheng Chen, En-Jui Kuo

Quantum Adaptive Self-Attention for Quantum Transformer Models

Read the original on arXiv Machine Learning →

arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 8

Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks

arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.

By Chi-Sheng Chen, En-Jui Kuo

llms benchmarks

arXiv AI

Aug 10

Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification

arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.

By Hao-Yuan Chen

llms

Hugging Face Trending Papers

Jul 14

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates. We study this problem using a compact 44.

llms nlp fine-tuning

arXiv Machine Learning

Jul 1

Beyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning

arXiv:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.

By Kung-Ming Lan

rag safety

arXiv AI

Jul 15

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

arXiv:2607. 12780v1 Announce Type: cross Abstract: Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates.

By Mehdi Saeedi, Eddie Richter, Paul Hartke

llms nlp fine-tuning

arXiv Machine Learning

Jul 30

Shot-based quantum encoding: a data-loading paradigm for quantum neural networks

arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.

By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov

rag benchmarks

One email a morning, machine-written

Related stories

Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks

Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

Beyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

Shot-based quantum encoding: a data-loading paradigm for quantum neural networks